Reward estimation with scheduled knowledge distillation for dialogue policy learning

Reward estimation with scheduled knowledge distillation for dialogue policy learning

Formulating dialogue policy as a reinforcement learning (RL) task enables a dialogue system to act optimally by interacting with humans. However, typical RL-based methods normally suffer from challenges such as sparse and delayed reward problems. Besides, with user goal unavailable in real scenarios...

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Bibliographic Details
Main Authors: Junyan Qiu, Haidong Zhang, Yiping Yang
Format: Article
Language:English
Published: Taylor & Francis Group 2023-12-01
Series:Connection Science
Subjects:
reinforcement learning
dialogue policy learning
curriculum learning
knowledge distillation
Online Access:http://dx.doi.org/10.1080/09540091.2023.2174078

Internet

http://dx.doi.org/10.1080/09540091.2023.2174078

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